This disclosure relates generally to fraud detection and risk management, and more particularly to connecting decisions and scoring of fraud, risk and customer management through customer transaction profiles.
For financial institutions, such as banks, credit card issuers, or the like, there is an increasing need to connect decisions across a number of related, yet isolated decision areas. Each of these decision areas represents a particular financial channel or service, and is commonly referred to as a “silo”, meaning that data, and algorithmic processing of that data, used to generate a decision for the particular financial channel or service is limited only to that one channel or service. Such decisions include fraud scores, risk scores, and/or customer management scores, among other decisions and analysis.
Currently, most fraud solutions focus on a silo problem to maximize the detection capabilities in that silo. For example, within a credit card silo, one type of fraud transaction profile analytics solution, known as the Falcon Fraud Manager, is a very sophisticated solution that detects counterfeit, lost and/or stolen, card not present credit cards. This credit card fraud behavior detection system also generates a fraud score between 1 and 999, where higher scores represent transactions having a greater likelihood of fraud.
Fraudsters grapple with analytic detection while attempting to commit fraud within the silo. Yet, many fraudsters have also determined that sometimes silos may be well protected, but the connections or interaction between or among silos may not be very well protected, or not protected at all. For example, a large inflow of dollars to a long-existing demand deposit account (DDA) may be viewed as a normal or a low-risk event. However, if this event is then followed with multiple transfers to newly established DDAs and subsequently by multiple ATM withdrawals, then this cross-channel (or cross-silo) set of transactions can point to a large fraud event which may not have otherwise been captured by each respective silo solution. A system and method that can detect and analyze activity across the channels or across that customer's services is better able to detect such fraud.
It is therefore important to connect decisions and events across channels/services. A customer may have many financial services, or there may be many different individual accounts associated with one customer. For example, to determine the risk associated with a particular customer, it is desirable to understand the risk associated with each of that customer's multiple DDA accounts. Of these multiple DDA accounts, one account might have Online Banking enabled, check enabled and ATM enabled, while another might have check and ATM enabled. Still yet another DDA account might only have check enabled. To most precisely develop a risk decision associated with the customer, the behaviors over the all of the accounts and their enabled services needs to be summarized and analyzed.